{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I ran the following notebook in a docker container with the following commands:\n",
    "\n",
    "```\n",
    "docker run -it -p 8888:8888 -p 6006:6006 -v `pwd`:/space/ -w /space/ --rm --name md waleedka/modern-deep-learning jupyter notebook --ip=0.0.0.0 --allow-root\n",
    "```\n",
    "\n",
    "The following code is adapted from http://scikit-learn.org/stable/user_guide.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Argmax and Argmin\n",
    "\n",
    "$$ \\underset{x \\in D}{\\operatorname{arg\\,max}} f(x) := \\{ x \\mid \\forall y \\in D : f(y) \\le f(x)\\} $$\n",
    "\n",
    "$$ \\underset{x \\in D}{\\operatorname{arg\\,min}} \\, f(x) := \\{x \\mid \\forall y \\in D : f(y) \\ge f(x)\\} $$\n",
    "\n",
    "See [Argmax and Max Calculus](https://www.cs.ubc.ca/~schmidtm/Documents/2016_540_Argmax.pdf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Supervised learning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generalized Linear Models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ordinary Least Squares\n",
    "\n",
    "$$ \\underset{w}{min\\,} {|| X w - y||_2}^2 $$\n",
    "\n",
    "where $$ w = (w_1, ..., w_p) $$\n",
    "\n",
    "If $ X $ is a matrix of size $ (n, p) $ this method has a\n",
    "cost of $ O(n p^2) $, assuming that $ n \\geq p $."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ridge Regression\n",
    "\n",
    "$$ \\underset{w}{min\\,} {{|| X w - y||_2}^2 + \\alpha {||w||_2}^2} $$\n",
    "\n",
    "where \n",
    "\n",
    "$$ \\alpha \\gt 0 $$\n",
    "\n",
    "$$ ||w||_2 = ? $$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Lasso\n",
    "\n",
    "$$ \\underset{w}{min\\,} { \\frac{1}{2n_{samples}} ||X w - y||_2 ^ 2 + \\alpha ||w||_1} $$\n",
    "\n",
    "where\n",
    "\n",
    "$$ ||w||_1 = ? $$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Elastic Net\n",
    "\n",
    "$$ \\underset{w}{min\\,} { \\frac{1}{2n_{samples}} ||X w - y||_2 ^ 2 + \\alpha \\rho ||w||_1 +\n",
    "    \\frac{\\alpha(1-\\rho)}{2} ||w||_2 ^ 2} $$\n",
    "    \n",
    "where $ \\rho $ is to control the convex combination of L1 and L2, a.k.a ``l1_ratio``"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Least Angle Regression (LARS)\n",
    "\n",
    "Least-angle regression (LARS) is a regression algorithm for\n",
    "high-dimensional data. LARS is similar to forward stepwise\n",
    "regression. At each step, it finds the predictor most correlated with the\n",
    "response. When there are multiple predictors having equal correlation, instead\n",
    "of continuing along the same predictor, it proceeds in a direction equiangular\n",
    "between the predictors.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn import linear_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "reg = linear_model.LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.fit ([[0, 0], [1, 1], [2, 2]], [0, 1, 2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.5,  0.5])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# based on http://scikit-learn.org/stable/auto_examples/linear_model/plot_ols.html#sphx-glr-auto-examples-linear-model-plot-ols-py\n",
    "# but added Ridge and Lasso\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# From http://matplotlib.org/users/usetex.html\n",
    "# Requires a working TexLive installation in PATH\n",
    "from matplotlib import rc\n",
    "# rc('font',**{'family':'sans-serif','sans-serif':['Helvetica']})\n",
    "# rc('text', usetex=True)\n",
    "\n",
    "import numpy as np\n",
    "from sklearn import datasets, linear_model\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "import math\n",
    "\n",
    "def fit_and_plot(data_X, data_y, test_ratio=0.0):\n",
    "    \n",
    "    if test_ratio == 0:\n",
    "        data_X_train = data_X\n",
    "        data_X_test = data_X\n",
    "        data_y_train = data_y\n",
    "        data_y_test = data_y\n",
    "    else:\n",
    "        print(data_X.shape)\n",
    "        sample_size, _ = data_X.shape\n",
    "        test_set_size = math.floor(sample_size * test_ratio)\n",
    "        \n",
    "        # Split the data into training/testing sets\n",
    "        data_X_train = data_X[:-test_set_size]\n",
    "        data_X_test = data_X[-test_set_size:]\n",
    "\n",
    "        # Split the targets into training/testing sets\n",
    "        data_y_train = data_y[:-test_set_size]\n",
    "        data_y_test = data_y[-test_set_size:]\n",
    "    \n",
    "    # Create linear regression object\n",
    "    lr = linear_model.LinearRegression()\n",
    "    rg = linear_model.RidgeCV(alphas = [.1, .3, .5, .7, .9])\n",
    "    ls = linear_model.LassoCV(alphas = [.1, .3, .5, .7, .9])\n",
    "    en = linear_model.ElasticNetCV(alphas = [.1, .3, .5, .7, .9], l1_ratio = [.1, .3, .5, .7, .9, .99, .997])\n",
    "    la = linear_model.LarsCV()\n",
    "\n",
    "    # Train the model using the training sets\n",
    "    lr.fit(data_X_train, data_y_train)\n",
    "    rg.fit(data_X_train, data_y_train)\n",
    "    ls.fit(data_X_train, data_y_train)\n",
    "    en.fit(data_X_train, data_y_train)\n",
    "    la.fit(data_X_train, data_y_train)\n",
    "\n",
    "    # Make predictions using the testing set\n",
    "    data_y_pred_lr = lr.predict(data_X_test)\n",
    "    data_y_pred_rg = rg.predict(data_X_test)\n",
    "    data_y_pred_ls = ls.predict(data_X_test)\n",
    "    data_y_pred_en = en.predict(data_X_test)\n",
    "    data_y_pred_la = la.predict(data_X_test)\n",
    "\n",
    "\n",
    "    # The coefficients\n",
    "    print('Coefficients: \\n', lr.coef_, rg.coef_, ls.coef_, en.coef_, la.coef_)\n",
    "\n",
    "    print('Super parameters: \\n', (), (rg.alpha_,), (ls.alpha_,), (en.alpha_, en.l1_ratio_), (la.alpha_,))\n",
    "    # The mean squared error\n",
    "    print(\"Mean squared error: \\n %.2f %.2f %.2f %.2f %.2f\"\n",
    "          % (mean_squared_error(data_y_test, data_y_pred_lr),\n",
    "             mean_squared_error(data_y_test, data_y_pred_rg),\n",
    "             mean_squared_error(data_y_test, data_y_pred_ls),\n",
    "             mean_squared_error(data_y_test, data_y_pred_en),\n",
    "             mean_squared_error(data_y_test, data_y_pred_la)\n",
    "            ))\n",
    "    # Explained variance score: 1 is perfect prediction\n",
    "    print('Variance score: \\n %.2f %.2f %.2f %.2f %.2f' % \n",
    "          (r2_score(data_y_test, data_y_pred_lr),\n",
    "           r2_score(data_y_test, data_y_pred_rg),\n",
    "           r2_score(data_y_test, data_y_pred_ls),\n",
    "           r2_score(data_y_test, data_y_pred_en),\n",
    "           r2_score(data_y_test, data_y_pred_la)\n",
    "          ))\n",
    "\n",
    "    # Plot outputs\n",
    "    plt.rcParams[\"figure.figsize\"] = [15.0, 10.0]\n",
    "    plt.scatter(data_X_test, data_y_test,  color='black')\n",
    "    # blue\n",
    "    plot_lr, = plt.plot(data_X_test, data_y_pred_lr, color='#4572a7', label='LinearRegression', linewidth=3, linestyle='solid')\n",
    "    # green\n",
    "    plot_rg, = plt.plot(data_X_test, data_y_pred_rg, color='#1a9850', label='RidgeCV', linewidth=1, linestyle='solid')\n",
    "    # orange\n",
    "    plot_ls, = plt.plot(data_X_test, data_y_pred_ls, color='#ff7f0e', label='LassoCV', linewidth=1, linestyle='solid')\n",
    "    # red\n",
    "    plot_en, = plt.plot(data_X_test, data_y_pred_en, color='#aa4643', label='ElasticNetCV', linewidth=1, linestyle='solid')\n",
    "    # purple\n",
    "    plot_la, = plt.plot(data_X_test, data_y_pred_la, color='#886fa8', label='LarsCV', linewidth=1, linestyle='solid')\n",
    "    \n",
    "    plt.legend(handles=[plot_lr, plot_rg, plot_ls, plot_en, plot_la])\n",
    "\n",
    "    plt.xticks(())\n",
    "    plt.yticks(())\n",
    "\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "All features are ['age', 'sex', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6']\n",
      "Using bmi\n",
      "(442, 10)\n",
      "(442, 1)\n",
      "(442,)\n",
      "(442, 1)\n",
      "Coefficients: \n",
      " [ 976.05247196] [ 848.21749059] [ 929.33232687] [ 815.21201628] [ 976.05247196]\n",
      "Super parameters: \n",
      " () (0.10000000000000001,) (0.10000000000000001,) (0.10000000000000001, 0.997) (0.0,)\n",
      "Mean squared error: \n",
      " 3700.73 3692.15 3687.88 3703.55 3700.73\n",
      "Variance score: \n",
      " 0.35 0.35 0.35 0.35 0.35\n"
     ]
    },
    {
     "data": {
      "image/png": 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1CkWZ16tRytsErVZY+AIc/N427rYcSjdwUuTOdTL2HP6hvezjHzpOSsdoHqws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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6d5cf57780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn import datasets\n",
    "\n",
    "# Load the diabetes dataset\n",
    "diabetes = datasets.load_diabetes()\n",
    "\n",
    "print(\"All features are %s\" % diabetes.feature_names)\n",
    "\n",
    "FEATURE_TO_USE = 2\n",
    "\n",
    "print(\"Using %s\" % diabetes.feature_names[FEATURE_TO_USE])\n",
    "\n",
    "print(diabetes.data.shape)\n",
    "\n",
    "# Use only one feature\n",
    "diabetes_X = diabetes.data[:, np.newaxis, FEATURE_TO_USE]\n",
    "diabetes_y = diabetes.target\n",
    "\n",
    "print(diabetes_X.data.shape)\n",
    "print(diabetes_y.data.shape)\n",
    "\n",
    "fit_and_plot(diabetes_X, diabetes_y, test_ratio=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(500, 1)\n",
      "Coefficients: \n",
      " [ 5.17019582] [ 5.15808425] [ 5.07888176] [ 4.97012335] [ 5.17019582]\n",
      "Super parameters: \n",
      " () (0.90000000000000002,) (0.10000000000000001,) (0.10000000000000001, 0.69999999999999996) (0.0,)\n",
      "Mean squared error: \n",
      " 106.42 106.40 106.33 106.26 106.42\n",
      "Variance score: \n",
      " 0.20 0.20 0.20 0.20 0.20\n"
     ]
    },
    {
     "data": {
      "image/png": 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SbNadd96JyWSq1L6LiIiIiIhj2X/o4Ngadr8kY3Mv+5Ts7GwGDx7MoUOHKCoqIiwsDIAu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\nBQAAAAAxRtACAAAAgBgjaAEAAABAjP0/oKnlGJWaulsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6d5ceef320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "SAMPLE_SIZE = 500\n",
    "\n",
    "t_dis = np.random.standard_t(100, size=[2, SAMPLE_SIZE])\n",
    "t_dis = np.random.normal(0, 1, size=[2, SAMPLE_SIZE])\n",
    "t_dis\n",
    "\n",
    "\n",
    "t_dis[1] = t_dis[0] * 5 + t_dis[1] * 10\n",
    "\n",
    "data_X = t_dis[0].reshape(-1, 1)\n",
    "data_y = t_dis[1]\n",
    "\n",
    "fit_and_plot(data_X, data_y, test_ratio=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(500, 1)\n",
      "Coefficients: \n",
      " [ 97.12339861] [ 97.09920249] [ 97.03618222] [ 96.95249591] [ 97.12339861]\n",
      "Super parameters: \n",
      " () (0.10000000000000001,) (0.10000000000000001,) (0.10000000000000001, 0.98999999999999999) (0.0,)\n",
      "Mean squared error: \n",
      " 914.87 914.84 914.75 914.64 914.87\n",
      "Variance score: \n",
      " 0.90 0.90 0.90 0.90 0.90\n"
     ]
    },
    {
     "data": {
      "image/png": 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IgAVEgCXaEwAAAEDt5eXlKS0tTRaLRWlpacrLy6vyeofLq4m3LVCW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\n4FqdMYgHiQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6d621528d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "SAMPLE_SIZE = 500\n",
    "\n",
    "data_X, data_y = datasets.make_regression(n_samples=SAMPLE_SIZE, n_features=1, n_informative=1, \n",
    "                                 n_targets=1, bias=15.0, effective_rank=None, \n",
    "                                 tail_strength=0.5, noise=30.0, shuffle=True, \n",
    "                                 coef=False, random_state=None)\n",
    "fit_and_plot(data_X, data_y, test_ratio=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficients: \n",
      " [ 0.50009091] [ 0.49603246] [ 0.49009091] [ 0.49463916] [ 0.50009091]\n",
      "Super parameters: \n",
      " () (0.90000000000000002,) (0.10000000000000001,) (0.10000000000000001, 0.10000000000000001) (0.0,)\n",
      "Mean squared error: \n",
      " 1.25 1.25 1.25 1.25 1.25\n",
      "Variance score: \n",
      " 0.67 0.67 0.67 0.67 0.67\n"
     ]
    },
    {
     "data": {
      "image/png": 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3TqBrxi4AGrVuSI+a7xAREYIajeCqmVC9fjhHL5MMLUmSJKkcy8kN8qeXPuXj\nRWtpXCCwdkbuIbT1dRLXZQMQ1+54ulZ/h0AAqN8WLp8M0bXDOHnZZmhJkiRJ5dD+nFz++OInzF6y\nnhMKBNa2SruI+e5NOmzYDUDzhLp0ipmSF1hxp8AvX4fKMWGcvHwwtCRJkqRyZF9OLnc/N4v5yzfQ\ntEBgbam0k9j1b9Jp814AWibWpn3V9/ICq+V5MPRFqFQ5jJOXL4aWJEmSVA7s3Z9D8jMzWbRiI80K\nBNbGqG00zniLpG37AGibGE2b6Fl5J3X4BQx6HCIiwzV2uWVoSZIkSWXY7r05jHnqA5au3kzz0P8C\na0PlzZy44m2aZ+V9sXCHxAAnR8/JO6n776BfCnmXs1QcDC1JkiSpDMres59b/z6Dr9du5cQCgbW+\nynckLJ1E8z15gdU5YTfNqy3OO6nXnXDm6HCNXKEYWpIkSVIZkrV7Hzc+Np2Mb3dwcoHAWls1k24L\nJ9M8mPfFwt3abSKuxjd5J537AHS7KlwjV0iGliRJklQG7Mjey3WPTCNz0y5aFQisNVXXcMYXU2lO\nCIBTWq+ice2NeScNfgoSR4Rr5ArN0JIkSZJKsW1Ze/jtw1PZtG03bULQ5vvAWh29ip7zp+cH1umt\nl3F87R15Jw1LhdYDwjWyMLQkSZKkUmnrzj2M+usUdmTtpW0IOnwfWKuiV9Br/gyaf39cr7bp1K2Z\nlffgkreh+ZnhGVgHMbQkSZKkUmTT9myu/Mtkdu/JoX0Q6nEgsJbTa/7M/MDq0/5LYqtn5z248n1o\nkhSegVUoQ0uSJEkqBb7buovL759Ezv4gCUGoeyCwqqbT64uP8wOrb4fF1IrZnffgms+gQZvwDKwf\nZWhJkiRJYZS5OYtL//QuESHoEITYA4FVZTG9FnyWH1jnJC6iRvReCETCdQsgtln4htZPMrQkSZKk\nMFi3cSeX3z+JiBB0DkLt7wNrddRCei6aQ3MgUCmCcxO+oFqVfRAdC9cshJoNwzu4joihJUmSJJWg\njA3bufKBKUSGoEsQan4fWGsqfcGZ/51HcyAquhL92swjuvJ+iG0BV06HmNjwDq6jYmhJkiRJJeCb\nzG1c9eB/iAxB1yDU+D6w1kWkcfqX82kOVK0RRd+Wc6gSlQONOsGlb0OVGuEdXD+LoSVJkiQVo8+X\nrOeu52ZRKQQ9ghDzfWB9y+eckr6I5kCN2Mr0aTGbqEq50KIPDH8ZoqqGd3AdE0NLkiRJKgZvf/wV\nj7+ZRnQmMh42AAAgAElEQVQI+gQD+ds3Bj+j27LFNAeOq1eJns0+p1JkENpdCEP+AZH+J3p54G9R\nkiRJKkKvfpDOs+8upNohgbVl/8ckfZ1Oc6D+8QFOazqXyIgQJP0aznsQIiLCN7SKnKElSZIkFYGX\npi3mX1MXU+OQwIrOyqDhmqk0Bxo3yqH7CQuIiAjB6bdC7zshEPjhRVVmGVqSJEnSMXju3YWM/yCd\nmocEVrUdK2iwbkb+44u6z81rqrP/CKdeH4ZJVZIMLUmSJOlneOLNNN76+CtqHxJY1bcto37mrPzH\n+YF1/qPQ+dIwTKpwMLQkSZKko/DQuM+ZOvcbYg8JrJpbvqTuhk/zH+cHVptBcPG/wjCpwsnQkiRJ\nko5Ayr8+YebCNdQ9JLBqbVpAnY1zAYiMyOWCbvPzdnS7Bs69PxyjqhQwtCRJkqQfceezM5mTnkn9\nIPQJ/S+wjts4j+M2fQFAtSp7OK/Tf/N2nDUWTrup5AdVqWJoSZIkSYW49e/vs2jFRo4/JLBiN8ym\n9pa8qKpTPYve7dPzdgx6Ajr+MhyjqhQytCRJkqQCfvu3qXy9diuNDwmsOt9+Qq2tSwBodNxWTm31\ndd6OEeOh5TnhGFWlmKElSZIkAVfcP4m1G3dywiGBVXf9TGpuXw5As/obSWqx6vsTpkJc9zBMqrLA\n0JIkSVKFNnzsRLbs3EPTQwKr/roZVN+xAoCWjTJJaLo2b8dvZ0P91uEYVWWIoSVJkqQK6fzfv8be\nfbk0D0LHAoHVYO17VNu5CoD2cWto1fjbvB03LobaJ4RhUpVFhpYkSZIqjFAoRL9bxwNwUhDiCgTW\n8RlTiNmVd9Wqc/NVNG+wMW/Hbd9ATGyJz6qyzdCSJElSuVcwsFoFoXGBwGq4ehLR2esB6H7SCk6o\nuyVvxx3roXK1Ep9V5YOhJUmSpHIrGAxxzui8wGoThIYFAqvRqrepunsDAKe1Wk7D47bn7bhrE0RG\nlfisKl8MLUmSJJU7ucEg545+FYCEXKhHgcD65k2q7tkEQK+26dStmZW34+6tEBFR4rOqfDK0JEmS\nVG7k5gY597a8wOqYC7EFAqvxytepsjfvbYFnJyymdrXdUKkq3Lk9LLOqfDO0JEmSVObtz8ml/5jX\nAEjKhVoFAqvJiteovG8bAOckLqJG9F6oezJcOzcss6piMLQkSZJUZu3dn8P5t08AoFsuVC8QWCd8\nPY6o/TsB6N9pITFV9kGLPvCrN8IyqyoWQ0uSJEllzu69OQy6Iy+wTsmF6AKBFffVy1TK2QXAwKQv\nqBKVAx1/BYMeD8usqpgMLUmSJJUZu3bvY8ideVekzsiFqIMC6yUq5ewGYHCXNKIqBeGM26B3clhm\nVcVmaEmSJKnU25G9l4vuehOAXrkQUSCwmi7/N5G5ewC4oOs8IiNDcN6D0PU3YZlVAkNLkiRJpdjW\nnXsYNnYihKBPMHDQvqbLXiQyuA+AC7vNIyIiBENfgLZDwjCpdDBDS5IkSaXO5u27GXHvW4UGVvyy\nF4gI7gfgou5zCQSAS96G5meGYVKpcIaWJEmSSo0NW3bxq5R3Cg+spf8kIpQDFAisUTOhUWIYJpV+\nnKElSZKksFu/aSeX/XkSgUID63kiQrlUishlSPf5eRuv/wJim4dhUunIGFqSJEkKm4wN27nygSmF\nBlaz9OcIEKR61T2c2/G/eRtvWQ41GoRhUunoGFqSJEkqcSvWb+Wah6YSUWhgPUuAEHVq7KR3u6V5\nG29fA1VrhmFS6ecxtCRJklRilmZs5vr/e+9HA6tx7FZOafl13sbkDRBVNQyTSsfG0JIkSVKx++/K\n77jliRlEFhpYzxAAmtX/jqQWq/M23r0FIiJLflCpiBhakiRJKjZfLP+WMU9/SKUfCaxWjTJp33Rt\n3sZ7tpF3O0GpbDO0JEmSVOQ+X7Keu56bRdSPBFZC3BpaNv4WajSEW7aHZ1CpmBhakiRJKjIfL1rD\nvS9+QuVDAiuQu49my18EoHPzb2jeYBOc0A1+vSxco0rFytCSJEnSMZsxfxX3p86myiGBFbl/F02/\nfhmA7id9zQl1t0LbC2DoP8M1qlQiDC1JkiT9bFM+X8HfXp1L1UMCK2rvNk5Y+RoAp7dexvG1d0CP\na6FfSrhGlUqUoSVJkqSj9tbHy3nizfnEHBJYVXZvpPGqiQD0aptO3ZpZcPa9cOoN4RpVCgtDS5Ik\nSUfsnuc/4rMv11HtkMCqums9jTImAXB2wmJqV9sNg5+CxBHhGlUKK0NLkiRJP2n032ewcMV31Dkk\nsGJ2ZnD82qkAnJO4iBrRe+EXr8HJfcM1qlQqGFqSJEn6Qb/921S+XruV+j9yF8H+nRYQU2U//Po9\nOKFruEaVShVDS5IkSYf5Vco7bNiyi4ZB6BP6X2BF5Owm/quXABiY9AVVonLgd3OgXstwjSqVSoaW\nJEmS8l145xvs3L2PxocEVsG7COYH1k1fQq0m4RpVKtUMLUmSJNH3lnEAxAWha6jgXQS/o/GqtwAY\n3DWNqMgg3JwONRuFZU6prDC0JEmSKrADgdU8CM0KBFZ01loarpkCwAXd5hEZEYLbM6BqrbDMKZU1\nhpYkSVIFdCCwTgpCXIHAqrZjJQ3WvQ/ARd3nEggAd34HlaqEY0ypzDK0JEmSKpADgdUqCI0LBFb1\nbcuonzkLKBBYd2+FiIhwjCmVeYaWJElSBXAgsNoFoUGBwKq5ZTF1N3wGwNAec/M2jt1e4vNJ5Y2h\nJUmSVI4dCKzEXKjD/wKr9qYviN04DzCwpOJgaEmSJJVDBwIrKRdqFQis2O/mUHvzQsDAkoqToSVJ\nklROhEIh+t06HoDuuVCtQGDV+fYTam1dAnwfWNWPh1sNLKm4GFqSpFIvNTWV5ORkMjIyiIuLIyUl\nhZEjR4Z7LKnUKBhYp+VClQKBVXf9TGpuX07VqH2c32MhxJ8OlxlYUnEztCRJpVpqaiqjRo0iOzsb\ngNWrVzNq1CgAY0sVXjAY4pzReYHVMxciCwRW/bXvU33nSmKrZ9GnRzp0/BUMmhWuUaUKJxAKhY74\n4KSkpNC8efOKcRxJkg4WHx/P6tWrD9vetGlTVq1aVfIDSaVAbm6Qc297FYA+uYGD9jVYM5VqWRk0\njt3KKS2/hl53wpmjwzGmVC4FAoG0UCiU9FPHeUVLklSqZWRkHNV2qTzbn5NL/zGvAYcH1vGrJxGT\nvZ4WDTbQqX0GDPo7dPSqrxQuhpYkqVSLi4sr9IpWXFxcGKaRwmPPvhwG/n4CcHhgNVr1NlV3b6Bt\nk3W06bAeRr4OJ50VjjElFWBoSZJKtZSUlIM+owUQExNDSkpKGKeSSkb2nv0MTn4dODywGn/zJlX2\nbKJTs1W0OH4jjPoQGnUs+SElFcrQkiSVagdueOFdB1WR7Mzex4V3vQEcHlhNVk6g8t6tdD/pa06o\nuxWuXwCxzcIxpqQf4c0wJEmSSomtO/cwbOxE4PDAOuHr8UTt38EZrZfRoPYOGL0SqtUJx5hShebN\nMCRJksqITduz+cW9b0MI+gQPCayvXiEqJ4s+7ZYQW2MX3JEJlWPCNKmkI2VoSZIkhUnm5iwu/dO7\nhQZW3FcvUSlnN/06/JeaMXvgrs0Q6X+6SWWF/7ZKkiSVsIwNO7jygcmFBlbT5f8iMncv/TstJKbK\nPrhnGwQCP7CSpNLK0JIkSSohK9dv4+qH/lN4YC17kcjgPgYmfUGVqBwYuz1MU0oqCoaWJElSMUtf\nvZkbHn2PQCGBFb/0n0SEchjSNY1KkUEDSyonDC1JkqRismjFd9z69xlEFBpYzxMRyuWCbvOIrFwF\n7twapiklFQdDS5IkqYjNXZpJ8jMzCw2sZunPESDIRd3nEmjYHq7eFqYpJRUnQ0uSJKmIfPzftdz7\nwsdEFhpYzxIglBdYbQfBxb5FUCrPDC1JkqRj9H7aKv7y8myiCg2sZwgAQ3vMhVNvgLOnh2dISSXK\n0JIkSfqZJn32Nf83YR6Vfyqwzv0rdDOwpIrE0JIkSTpKr89cxtNvf0HVnwqsi/8NbQaGZ0hJYWVo\nSZIkHaGX3lvMv/6zmOhCAqt5+jPA94F1xVSI6x6OESWVEoaWJEnST3j23QW8+sFSqv1AYEVX3seA\nHgvhd3OgXsswTSmpNDG0JEmSfsBjr8/jnU+/psYPBFad6ln07pEONy+Fmg3DNKWk0sjQkiRJOsRf\nXv6M99NWU+sHAqtJ7BZ69FgBt6+BqjXDNKWk0szQkiRJ+t49z3/EZ1+u47gfCKwTj99Axx4ZcOdG\nqFQ5TFNKKgsMLUmSVOHd+vf3WbRiI3V/ILDanrCWNj0y4e6tEBERpikllSWGliRJqrCufvA/rMzc\nRv0g9AkVCKxgLs2XPU+n5qto0WMjjN0eviEllUmGliRJqnB+lfIOG7bsouEhgRWZk03Tr1LpfvLX\nnNBjq4El6WcztCRJUoUxOPl1svfsp8khgRW1dxsnrHyNM1ovo0GPHQaWpGNmaEmSpHKv7y3jAGga\nhBMLBFaV7A00Xv02fdovIfacOnDTmnCNKKmcMbQkSVK5dSCwmgehWYHAis5aS8M1U+iX+F9qntYD\nLlkfrhEllVOGliRJKncOBNZJQYgrEFjVdqykwbr36d9pITGDRsL508M1oqRyztCSJEnlxoHAah2E\nRgUCq8a2ZdTLnMXApPlUuTwZTjewJBUvQ0uSJJV5BwKrXS404H+BVXPzf6n73WyGdE2j0k1PQofh\n4RpRUgVjaEmSpDLrQGAl5kKdAoFVe9N8YjemcWG3eUSMfQNa9A7XiJIqKENLkiSVOQcCKykXahUI\nrNjv5lB780Iu6j6XwF9nQcMO4RpRUgVnaEmSpDIhFArR79bxAHTPhWoFAqvOt59Qa+uSvMB6YhEc\n1zRcY0oSYGhJkqRSrmBgnZ4LlQsEVr31H1Jj+1cM7TEXbvsGYmKPev3U1FSSk5PJyMggLi6OlJQU\nRo4cWVTjS6qgDC1JklQqBYMhzhmdF1i9ciGiQGDVX/s+1XeuzAus5G8hKvpnPUdqaiqjRo0iOzsb\ngNWrVzNq1CgAY0vSMQmEQqEjPjgpKSk0b968YhxHkiRVdLm5Qc697VUA+uQGDtrXYM1UqmVl5AXW\n3VsgIvKYnis+Pp7Vq1cftr1p06asWrXqmNaWVD4FAoG0UCiU9FPHeUVLkiSVCvtychkw5jXg8MBq\nuHoS0dnrGNpjHtyzDQKBwpY4ahkZGUe1XZKOlKElSZLCas++HAb+fgJweGA1WvU2scEM+ndaBGO3\nF/lzx8XFFXpFKy4ursifS1LFYmhJkqSw2LVnP0OSXwcOD6zG37xJ46hv6JW4tFgC64CUlJSDPqMF\nEBMTQ0pKSrE9p6SKwdCSJEklakf2Xi66603g8MBqsmICzWusoHPnTCrfnVnssxy44YV3HZRU1LwZ\nhiRJKhFbd+5h2NiJwOGBdcLX42ld5yvi2sdQ59bPwzGeJB0Rb4YhSZJKhY3bshn5x7chBH2CBwdW\n3Fcvk9hwGdXPa88Jv/1vmCaUpKJnaEmSpGKRuTmLS//07g8E1kt0i0snOGwAJ17yVpgmlKTiY2hJ\nkqQilbFhB1c+MLnQwGq6/F+c1uJLsq74Lc0vfCdME0pS8TO0JElSkVixbivXPDyVQCGBFb/sBXq2\n+i+bb/gTTfpdEqYJJankGFqSJOmYpK/ezA2Pvld4YC39J2e3W8iG5OdocNp5NAjTjJJU0gwtSZL0\nsyz8egOjn/yAiEID63nO67CA9X96i9hO3YkN04ySFC6GliRJOipzl2aS/MxMIgsJrGbpzzKg0xes\ne+RjarRqRcswzShJ4WZoSZKkI/LxojXc++InVPqBwBqclMa6pxcRE9+Ek8I0oySVFoaWJEn6UdPT\nVvHAy7OJKjSwnuGCrmlkvvA1lRvWoVmYZpSk0sbQkiRJhXr3s695dMI8Kv9AYF3UbR4bUtdQqW4N\nTgjTjJJUWhlakiTpIBM+XMo/3llA1UICq3n6M1zUfS6bX80kolYMDcM0oySVdoaWJEkC4KVpi/nX\n1MVE/0hgbZuwgUCNqtQN04ySVFYYWpIkVXDPvLOA1z5cSrUfCKyhPeay882NBGIqc1yYZpSkssbQ\nkiSpgvq/CXOZ9NkKavxAYJ3SbRXHvZUOVaOoEaYZJamsigj3AJIkqWilpqYSHx9PREQE8fHxpKam\nHrT//tTP6HvLOD7+dAV9cgN0LRBZzdOfoWf1Rxn47n9ofO/XxFSNKunxJalc8IqWJEnlSGpqKqNG\njSI7OxuA1atXM2rUKACW7jmBz5esJ7aQK1gtlj5NVKcanDdpKlGVIkt8bkkqbwwtSZLKkeTk5PzI\nOqDNgFt5cX4kdUPrDwus9isfY12bkxgyaTqRkb7RRZKKiqElSVI5kpGRkf/PvX73HJWqxNAgCO1y\n/xdYgWAOXdb/H7NbnMY5Ez8iIiJQ2FKSpGNgaEmSVI7ExcVx8oX3A9AwCG0KBFZkTjanbXmUN5oM\npOdrn9MrYGBJUnExtCRJKif63jKOky+8n2ZBaB76X0RF7d1K792P82S9y7gldT7dDSxJKnaGliRJ\nZVzfW8YBcFIQ4kIHX8Hqv/cv3Ffrem5+cjFPh2tASaqADC1JksqoA4HVKgiND7mC1T/3byTXHM2t\njy8l9YcWkCQVG0NLkqQy5kBgtQtCgwKBVSV7A4MrPcK1MWO5+aFlTAjXgJIkQ0uSpLLiQGAl5oao\nw/9uxR6dlcFF1R7jihr3c+NDK3k3XANKkvIZWpIklXIHAispN0QtIoC8q1jVtn/N8Ni/84taD3L9\nQxn8J4wzSpIOZmhJklRKHQis7rkhqhUIrBpblzCywdMMjX2Eax9ax7QwzihJKpyhJUlSKXMgsM7I\nhSgCHAisWpsX8qsmzzAk8jF++8C3BpYklWKGliRJpUAoFKLfreMB6JN78PdcHbdxHpfGP8/5kU9w\n9Z83GliSVAYYWpIkhVEwGOKc0YUHVp1vP+P8Fm/wq8gHGJXyVwNLksoQQ0uSpDDIDQY5d/SrwOGB\nVTdzFh1bzOW+E8dw5X2PGFiSVAYZWpIklaCc3CDn3VZ4YNVf9z51TlzP6x2u4td3PMEb4RhQklQk\nDC1JkkrAvv25DLj9NeDwwGqwZir7Twoy95QR/P2mfgwMx4CSpCJlaEmSVIx2793PoDteBw4PrONX\nT2LdyXVJP/tX/OXqXuEYT5JUTAwtSZKKQdbufVxwZ96b/w4NrEar3mLhyS3ZO3gUd196WjjGkyQV\nM0NLkqQitHXnHoaNnQgcHliNv3mDmSd3I2rEjTw4rGs4xpMklRBDS5KkIrBpeza/uPdt4PDAarLi\nNd5peRY1rxjDM4M6hWM8SVIJM7QkSToGmZuzuPRP70II+gQPDqwTvh7Hqy0HcsHv7mHcOe3DNKEk\nKRwMLUmSfoaMDTu48oHJhQZWq9XP81TzS7j8lj8xsVfrME0oSQonQ0uSpKOwYv1WrnloaqGB1Snz\n7/y1ydW0Hf0EU045MUwTSpJKA0NLkqQjkL56Ezc8Op1AIYF16qa/cW+DG+k8+gWmJTUL04SSpNLE\n0JIk6Ud88dUGxjz1QaGBdfb2+7k9dgyn3vYa0xJOCNOEkqTSyNCSJKkQny9Zz13PzSKikMA6P/sP\n3FjjbnqPmcy0Vg3DNKEkqTQztCRJKmDmggxS/v0pkYUE1gX77uZ30X/g3DEfMq1F/TBNKEkqCwwt\nSZKAqXNW8tD4OVQqJLB6B+8nOWoMg2//lGlxdcI0oSSpLDG0JEkV2sSPlvP3ifOJCoXoE4w4aF8C\nT/K3yKu5eMwHTGtYO0wTSpLKIkNLklQhvfL+Ev45eRFVgrn0CVUC/ncVq1FgHP+OGMYvb3+HafVq\nhG9ISVKZZWhJkiqU5yYtZPyMdKJD++kTrEzBvwqrRE5lMn35d/JL/Cq2WviGlCSVeYaWJKlCePyN\nNN7+5CtiQnvpE6wKVM7flxU5h8/pwiv3PMGNNaPDN6QkqdyI+OlDJEkqu/7y8mf0vWUcH368gD65\nAXoEq+bvWxuxnPcjQ4y+909Me2g4dYwsSUUsNTWV+Ph4IiIiiI+PJzU1NdwjqYR4RUuSVC7d/dws\nZi9ZT93gVvqEYoHq+fu+jPiWbwMNePO+MVSLrvzDi0jSMUhNTWXUqFFkZ2cDsHr1akaNGgXAyJEj\nwzmaSkAgFAod8cFJSUmhefPmFeM4kiQdm5sfn87ibzbRJPgtLUMHf5nw5xHbyQrU5O0/X0TVyv6/\nRknFKz4+ntWrVx+2vWnTpqxatarkB1KRCAQCaaFQKOmnjvNvGUlSuXDVg1P4JnM7J+eupg/xwP8i\na1bEXvYHKvPuX35N5UqRYZtRUsWSkZFxVNtVvhhakqQy7Rf3vsWm7btJzFlOn0BLID5/34zIXEJE\nMOWBXxIZ6ceSJZWsuLi4Qq9oxcXFhWEalTRDS5JUJp3/+9fYuy+XU/YvoUNEWwi0zN/3fmTe2+L/\n89cRREQEfmgJSSpWKSkpB31GCyAmJoaUlJQwTqWSYmhJksqUvreMA+CsvYsIVeoAEW3z9x0IrKkP\nDiMQMLAkhdeBG14kJyeTkZFBXFwcKSkp3gijgvBmGJKkMuFAYPXfs5A9UYn52wPBHKZH5X3uysCS\nJBU3b4YhSSoXrrh/Ems37mTA7jnsrtwtP7Iic7KZViUaIiOZ9tDwME8pSdLBDC1JUql08T1vsi1r\nL0OyZ9OySg92V+4GQJV9m5kcHQuR0QaWJKnUMrQkSaVK/zGvsj8nyNCs2WyJ7sGOKj0AqLZvIW9H\nJ0B0rIElSSr1DC1JUqlw4DNYw3fOZWNMV7ZE5wVW5f0LmVI1AaITDCxJUplhaEmSwio/sLYvYGP1\njmyM6Zq3I2ch71dJoHr1zky778IwTihJ0tEztCRJYXEgsIZuS2dLjTZsrN4RgD3BL/kkqg3163Zn\n2p0DwzmiJEk/m6ElSSpRBwJr0LaVZNVowZYabQDYGlrG/Eon06xJD6bdem44R5Qk6ZgZWpKkYhcK\nheh363gCBBmwPZPd1ZuQVaMFAJmsZElkM9rEn8K0684K86SSJBUNQ0uSVGwOBFYlcjhv5xb2xjRg\nd/UmAKwMrOWbiMYktTqFab85M8yTSpJUtAwtSVKRyw0GOXf0q0SHdnPO7r3sr3Ice2MaALAk8B2Z\nEfU4o0N3nr7k1DBPKklS8TC0JElFJjc3yLm3vUotttF3TxS5UdXZXyUGgIUR29gUqMU5Xbvx4rCu\nYZ5UkqTiZWhJko7Z/pxc+o95jcah9Zy1vx6hyOPIjcrbNy8ii+2Bagw5vQvXDO4U3kElSSohhpYk\n6Wfbuz+H82+f8P/t3XmcV3Wh//HXOcM6rLKvM8PO4MY2Yi6ggSIqELhLauZtbt6We8tbdkPzlo5W\nZqZWXi0zvc21m+Wau6CiuLCJoCDINsO+r7Mz8/n9Mfj1581S9AtnltfzLzyfj8ObHmW8/MKBQbzH\n2OqBQE9CRu3ZG3E5+6LmXDQ2j8vPPCbRnZIkHW6GliTpoJVVVDH5+3/hOBYwtnoEMDB19mpcSVnU\nlMvPzOOisUOSGylJUoIMLUnSJ7avrJKp1zzEOF5kbPWpwIjU2exoP+VxBldOPo4powclN1KSpDrA\n0JIkfazd+yo477qHOZ9HGFs9hcCpqbOX4xoqo4h/PXcUZ32uf4IrJUmqOwwtSdLftWNPGRf+8FHy\nq+9nLJexnSmps1lxDVVRxNUXf46xI3KSGylJUh1kaEmS/saWnSV88YbH+V71HYzlm6zkstTZrDhQ\nFcEPLjuJk47pneBKSZLqrjjpAVJdVVhYSE5ODnEck5OTQ2FhYdKTpENuw7a9nH7VH1l33QmMrY6Y\nyzdTZy/FgRkZgeu+Mppnb7nQyJIk6R/wEy3pIxQWFpKfn09paSkARUVF5OfnAzBt2rQkp0mHRPHm\n3fzTT5/i1+XXMrbpDTwXfz919mIcqI7gp1eeytD+XRNcKUlS/RGFED7x5ZEjR4Z58+YdwjlS3ZCT\nk0NRUdHfPM/OzmbNmjWHf5B0iKzcsJMrb3mGX5b8gIdbXP+hsxfiQE0Ev/jGOIbkdEpooSRJdUsU\nRfNDCCM/7p6faEkfobi4+KCeS/XNu8Xb+eZtz3LL7gLGtr72Q5E1Mw6ECH71rdMZ0KtDgislSaq/\nDC3pI2RlZX3kJ1pZWVkJrJHSZ9HKLXz3189x/bZbGHvE1TzZ+trU2fuBdde/n0Gf7u0TXClJUv3n\nyzCkj1BQUEBmZuaHnmVmZlJQUJDQIumzmbdsIxOvup89P5jIqdVNmHnE1amzGQdecvG7/ziLZ2+5\n0MiSJCkN/ERL+gjvv/Bi+vTpFBcXk5WVRUFBgS/CUL3z6tvruOXeZ7hy7X2c1ONrzO787dTZjDhA\nBPdPP5tuHVonuFKSpIbHl2FIUgP0wptF3FP4Vy5Y+TBLsr/yobP3A+uBH0ymY7uWCS2UJKl+8mUY\nktQIPTNnFX958GHGLXuBo/te8qHImpFR+y/W/vTDL9C+dYukJkqS1CgYWpLUADz6ynJmPfZHRix5\nk379L2R130tSZ+8H1l+un0qbzGZJTZQkqVExtCSpHvvTC0t59+l76bdkJV36nc/a/oNSZ+8H1iMF\n55DZomlSEyVJapQMLUmqh+5/ZjG7Z95Bh6U7ad73XNb1G5E6ez+wHrvpXFo08x/zkiQlwf8HlqR6\n5O7HF9Jm9o3ES2NK+0yhtG/t82oCL2bUfvuvPzmPZk0ykhspSZIMLUmqD27781yGzP8+LZd2oTjn\nHOhT+7yCGl7JiAB48qfn0yTDPx5RkqS6wNCSpDrsx4WvcdY7X+OIpUexIPufIKf2eQnVvJ4RAxFP\n3Xw+GbGBJUlSXWJoSVId9IN7ZvEvKy+h27KxPNn7asiufb47qmZeHAMxT998AXEcJbpTkiR9NENL\nkuqQ7/x6Jj9adz59lk/hvl4/hd61z7dH1Sw8EFjP/OwCosjAkiSpLjO0JKkO+Jdbn+Gnmy8hd9U5\n3GM01EcAACAASURBVN7jV9Cr9vmWqIbFcYSBJUlS/WJoSVKCLr/xMW7dmc+I4vP4Vfc7oEft8w1R\nDUvjCIh49pYLE90oSZIOnqElSQm465G5XPTq2Zyw9mLu6nobdK99vi6qYZmBJUlSvWdoSdJh9OsH\nZzNt7jm0WDmBO3vfDl1rn6+JAitjMLAkSWoYDC1JOgxuL5zJFxd8kcyiM/mvXrelXnLxTlTDpjgi\nIvDsLRclO1KSJKWNoSVJh9AvfvdXLl78VdqsO4vf9Lw19ZKLN+PAjgjWLZrB0ufvITMzk8LhNUyb\nNi3ZwZIkKS2iEMInvjxy5Mgwb968QzhHkhqGW+76Excv+Xee2ngWW3uckno+Pw7simDVGw+zcvaf\nPvT3ZGdns2bNmsM7VJIkHZQoiuaHEEZ+3D0/0ZKkNPrZHfdy/rIf0X7r2dzf/ebUWwTnxYHdEVx6\nxlFcOv4YPupfchUXFx/mtZIk6VAxtCTpMwoh8Iuf/5LJK35B+50TeaDbj1NvEZwTB/ZG8JWzh3Le\nqYMBuCYri6Kior/5OllZWYdztiRJOoQMLUn6lEII3PHjmxi/+ve03jeRB7vcCN1qz96IA/si+NqU\n4Uw+aeCH/r6CggLy8/MpLS1NPcvMzKSgoOBwzpckSYeQoSVJB6mmJnBXwfc5efUjNK+YyCOdr4fM\n2rPX40BJBN86L48Jx/f7yL///RdeTJ8+neLiYrKysigoKPBFGJIkNSC+DEOSPqHqmhru/9E3GLp6\nFm9UT2R3p2NTZ6/GgbIIvnvRKMaN7JPgSkmSdCj5MgxJSpPq6hoe/M9L6bPqHcozzubZDtekzmbH\ngfIIrrn0BEYf6++xkiRJtQwtSfo7qvZX8+x1X6Dtio3saDGRos4TU2evxIGKCH54+cl87qieCa6U\nJEl1kaElSf9HZVU1c677PBXLKljTdiL7uvdPnb0cByojKPjKGPIGd09wpSRJqssMLUk6oLyiitXX\nj2T1ojas7DyR0t45qbNZcaAqgp989VSGDeia3EhJklQvGFqSGr2ysnL23DiY1+bnUNzjUsr69kqd\nvRQH9kfw86+N5ai+nRNcKUmS6hNDS1KjVbJvH01/0pvH54xgQ/a3qBjYLXX2YhyojuD2fz2NwVkd\nE1wpSZLqI0NLUqOzZ9d2Mm/pzxNzRrC+zw1U5nZKnb0fWHd+ezz9eh6R4EpJklSfGVqSGo1dWzaQ\neftRPD13OOv6/oSq3HapsxfiQE0Ed39nAjnd2v2DryJJkvTxDC1JDd7O9Stp+evjeHbeMIr730x1\nbuvU2cw4ECL43ffOpFfntgmulCRJDYmhJanB2r7qLVr+ZizPLRhG0cBbqcltDkANgRdjCBHc9/2z\n6d6x9cd8JUmSpINjaElqcLYtmU2L+6YwY+GxrB58O+RmAFBF4OUDgfWHaybS5YhWCS+VJEkNlaEl\nqcHY+uZTNPufy5m5+BhWD74DcmuflxOYHQMRPHDdZDq2bZnoTkmS1PAZWpLqvS2v/pEmf/k2M985\nmjWDb4PBtc9LCLx+ILD+9z+/wBFtWiS6U5IkNR6GlqR6a8uMO+GvBbyw9CjWDL41FVh7Ccw5EFh/\nvn4KbTObJ7pTkiQ1PoaWpHpn6+PXU/Xcb3j5vSNZM+iWVGDtIjD/QGA9fMNUWrVsluhOSZLUeBla\nkuqNbX/6FqWzHuHVlbkUDboZBtU+305gYe37Lnj0xnNo2bxpciMlSZIwtCTVAzt+fwm73niNOUWD\nKBr441RgbSWw6EBgPXbTubRo5j/SJElS3eDPSiTVTSGw584z2PjmGhasH0jxwAIYWHu0KQq8E9d+\n+68/Po9mTTOS2ylJkvQRDC1JdUtNDWW3DmflO5Us3jyA4gE/SgXWhiiwNIY4jnjipnNp2sTAkiRJ\ndZOhJaluqK5i/005LFregXd39GNt/4ugbe3R2iiwPIbM5k146vqpZGTEyW6VJEn6GIaWpGRVlUFB\nN15f3pdVe0axtv8F0KH2qCgKrIihQ5sWPPWDSWTEBpYkSaofDC1JySjbBT/JZtaSgawtO411/c6D\nLrVHq6PAqhh6dGzN0987iziOkt0qSZJ0kAwtSYfX3s2Enw1kxuIhbNo/nvV9z0kdrYgCRTH06d6O\nZ646gygysCRJUv1kaEk6PHasJtw2lKfePJod0QTW95mSOloeBdbGkJvdkWe+Mc7AkiRJ9Z6hJenQ\n2vQ2NXeeyGNzh7G32Vls6Ds5dfRuFFgfw9D+Xbjnys8nOFKSJCm9DC1Jh0bRa1TfM4GH3hhJWeZE\nNg6cmDpaEgU2xjBqSA/uvWJ0giMlSZIODUNLUnotf4b9/30hD88ZQWmryWzKPTN19HYU2BzDmGN7\nc9+lJyY4UpIk6dAytCSlx1v/S+WDV/Lo3OGUtv4Cm3LPSB0tigNbIzg9rw//fuGoBEdKkiQdHoaW\npM/m9Tspf/waHp83jJI2U9mce1rqaGEc2B7BxBP6841zRiY4UpIk6fAytCR9OjNvoPS523hiwbHs\na3suW3I/eJnFm3FgRwTnjBnEP08aluBISZKkZBhakg7OY99k7+w/8vTCY9jb7jy25o5JHc2PA7si\nuGjcEC6fcEyCIyVJkpJlaEn6RML/XMCeN1/i2UVHsaf9BWzLPSl1Ni8O7I7gsjOOZtppRya4UpIk\nqW4wtCT9fSEQ7j6FHcvfY+bbQ9jV4UJ25H4udTwnDuyNIH/iUM49ZXCCQyVJkuoWQ0vS36qpJvx8\nCFvXl/DSksHs7HgRO3OPSx2/EQf2RfD1KSOYdNKABIdKkiTVTYaWpA/sr4AburBhRztmLxvIjk4j\n2JU7PHX8WhwojeBb5+cxYVS/BIdKkiTVbYaWJKjYCzf1omhrB+asyGN7l+PYnXts6vjVOFAWwdUX\nH8/YETnJ7ZQkSaonDC2pMSvZDjf3ZeWmzixYnce2riewJ/eDl1nMjgPlEVxz6YmMPrZ3gkMlSZLq\nF0NLaox2r4Nbj+Td9d1YXJzH1u6j2Zs7KHX8ShyoiOCHXz6Zzx3ZM8GhkiRJ9ZOhJTUmW5fDr/JY\nXNyTd9fnsbnHqZTk9k8dvxwHKiO48StjGDm4e4JDJUmS6jdDS2oM1s+H33yeBauyWbk5j029TqM0\nNyd1PCsOVEXw0ytPZWj/rsntlCRJaiAMLakhW/kC/PcXeH15X9Zuz2Nj7wmU5fZKHb8UB/ZHcOvX\nx3Jkn84JDpUkSWpYDC2pIXrnEXjwMmYtGcim3XlsyJ5ERZcPPql6MQ5UR3DHv57GoKyOCQ6VJElq\nmAwtqSGZdy/h8X/j+cVD2FmSx/o+U6ns8UFIvR9Yd357PP16HpHgUEmSpIbN0JIaglk/I8y4niff\nPIaSijzW9juf/c3apY5fiAM1EfzmOxPI7tbuH3whSZIkpYOhJdVnT32Pmtfv5NG5w6mqzqO4/8VU\nN22VOp4ZB0IEv/vemfTq3DbBoZIkSY2LoSXVR3/+MtWLHuKhN0YSOI6igZdQk9EcgBoCL8YQIrjv\n+2fTvWPrhMdKkiQ1PoaWVF+EAL8/m/2rXuXhOSMI5LF68JchigGoIvDygcD6wzUT6XJEq4/5gpIk\nSTpUDC2prqupgV+OoHJLEY/OHU6I8lide0XquJzA7BiI4IHrJtOxbcvktkqSJAkwtKS6q7oKfpxN\neUkFj88fRk3UhTW5X04dlxB4/UBg/emHX6B96xbJbZUkSdKHGFpSXVNZCjd2p7SiGU8sOJaaqAlr\nci9PHe8hMPdAYP35+im0zWye3FZJkiR9JENLqivKdsJPcthb1pynF+ZREzdlTe6XUse7CMw/EFgP\n3zCVVi2bJTZVkiRJ/5ihJSVt7ya4ZRC7Slry3KI8quNmFOVeljreTmBhRu23H73xHFo2b5rQUEmS\nJH1ShpaUlB2r4PZhbN/biplv51Gd0Zyi3EtTx1sJLDoQWI/ddC4tmvk/V0mSpPrCn7lJh9umxfBf\nJ7F5dxtmLcljf0ZLinO/+MFxFHin9o3t/PUn59GsSUZCQyVJkvRpGVrS4VL0Ktw7gfU72vPqsjz2\nN2lFce7FqeP1UeDdGDLiiCduOpemBpYkSVK9ZWhJh9qyp+GBCyja2oE5K/KoatqatbkXpY7XRoHl\nMbRq0ZSnfjSFjIw4wbGSJElKB0NLOlQWPgCPfJUVmzrz5uo8qpq2ZW3uBanjoiiwIoYObVvw1LWT\nyIgNLEmSpIbC0JLS7dVfwrPTeXd9NxYX51HZrD3rcs9LHa+KAqtj6NGpNU9ffRZxHCU4VpIkSYeC\noSWly/M/hFd+zuKiXry7IY+K5h1Yn3tO6nhFFCiKoW/39jxz1XiiyMCSJElqqAwt6bN69Gvw5h+Y\nvyqbVZvzqGjRifW5U1LHy6PA2hiGZHfkmW+MM7AkSZIaAUNL+rQKz4P3nuW15f1Ytz2P8pZd2JA7\nOXX8bhRYH8PQ/l2458rPJzhUkiRJh5uhJR2MEOCuk2HTYl5aMpAtu/Moy+zGxtyJqStLosDGGI4f\n0oN7rxid4FhJkiQlxdCSPomaarhlEGHfVp5fPIRdJXmUturJptwzU1fejgKbYxgzNIv7LjkhwbGS\nJElKmqEl/SNV5VDQlRDgiQXHUFaZQ0nrLDbnjk9dWRQHtkYwPq8PV104KsGxkiRJqisMLemjVOyF\nm3pRUxPxyNzhVNdksK9NH7b0G5e6sjAObI9g0okD+PrUEQmOlSRJUl1jaEn/v5JtcHM/qmsiHnoj\nD4B9bfuxpecHL7N4Mw7siODcMYPInzQsqaWSJEmqwwwtCWDXWvjFUeyvjnl4Tm1g7Wk3kG09xqSu\nzI8DuyK4aNwQLp9wTFJLJUmSVA8YWmrctrwLvx5F5f4MHp17ILDa57Kt+0mpK3PjwJ4ILp9wNBeN\nOzKppZIkSapHDC01TuvmwW/HUl7ZhMfn1wbWrg5Hs6Pr8akrc+LA3gjyJw3l3DGDk1oqSZKkesjQ\nUuOyYgb8YSqlFc14YkFtYO3seCw7uxyXuvJGHNgXwdenjmDSiQOSWipJkqR6zNBS4/D2Q/Dny9lb\n1oKnF9YG1o7OI9jVaXjqymtxoDSCb59/HGeM6pvUUkmSJDUAhpYatrm/hSeuYldJS55bVBtY27sc\nx+6Ox6auvBoHyiK4+uLjGTsiJ6GhkiRJakgMLTVML90ML9zA9r2tmPl2bWBt63oCezp88DKL2XGg\nPIJrLzuRk4/pndRSSZIkNUCGlhqWJ78Lc+5i8662zFpaG1hbuo9mX/tBqSuvxIGKCH745ZP53JE9\nk1oqSZKkBszQUsPwp8tgySOs39GeV5fVBtbmHp+npF2/1JWX40BlBDfmj2HkoO5JLZUkSVIjYGip\n/goB7p0Axa9RtLUjc1bUBtamXqdR2iYndW1WHKiK4OYrT+XY/l0TGitJkqTGxNBS/VNTA7cPhV1F\nrNjUmTdX1wbWxqwzKWv1wS8FfCkO7I/g1q+P48g+nZJaK0mSpEbI0FL9UV0FN/WC/eW8u74bi4vz\nCMCG7ElUZH7wSdWLcaA6gl/+2+kM7N0hub2SJElqtAwt1X2VJXBjDwAWFfVi2YbuBGB9n6lUtuiY\nuvZCHKiJ4M6rxtOvxxEJjZUkSZIMLdVlpTvgp30AmL8ym1VbuhCAtf3OZ3+zdqlr7wfWb74zgexu\n7f7OF5MkSZIOH0NLdc+ejfDzwQC8trwf67Z3IBBRPOBiqptkpq7NjAMhgnu/dxY9O7dJaq0kSZL0\nNwwt1R3bV8IdwwF4aclAtuxuRyBizcBLCRnNAKgm8FIMIYL7vn823Tu2TnKxJEmS9JEMLSVv4yK4\n62RCgOcWHcnu0kwCMasHXw5RDEAVgZcPBFbhtZPo3D7zY76oJEmSlBxDS8lZ8wr8/ixCgCcWHEtZ\nZTNCFLM694rUlXICs2Mgggeum0zHti2T2ytJkiR9QoaWDr93n4Q/XkRNTcQjc4dTXZNBTZTBmtwv\np66UEHj9QGD96YdfoH3rFsntlSRJkg6SoaXD581CePRfqK6JeOiN2j9kuCZqwprcy1NX9hCYeyCw\n/nL9VNpkNktorCRJkvTpGVo69GbfDs9dS1V1zCNzDgRW3JQ1g76UurKLwPwDgfVwwTm0atE0ma2S\nJElSGhhaOnSeuw5m/4LKqgwenVcbWNVxc4oGXZq6sp3Awozabz964zm0bG5gSZIkqf4ztBqRwsJC\npk+fTnFxMVlZWRQUFDBt2rT0f0eP/AssLKS8sgmPzz8QWBktKBp4SerKFgKLDwTW4z8+l+ZN/a+i\nJEmSGg5/dttIFBYWkp+fT2lpKQBFRUXk5+cDpC+2/nsqrJxBSUUznlxQG1j7M1pSPPCLqSsbo8CS\n2je289efnEezJhnp+b4lSZKkOiQKIXziyyNHjgzz5s07hHN0qOTk5FBUVPQ3z7Ozs1mzZs2n/8Ih\nwJ0nwpZ32FvWgqcXHg3A/iatKB5wcera+ijwbgxNM2IevelcmmTEn/77lCRJkhISRdH8EMLIj7vn\nJ1qNRHFx8UE9/1jV++GWgVC6nV0lLXluUe0nWFVNW7O2/0Wpa2ujwPIYWrVoylM/mkKGgSVJkqRG\nwNBqJLKysj7yE62srKyD+0JV5VDQFYBte1vzwtvvB1Zb1va/IHVtTRRYGUOndi156pqJZMQGliRJ\nkhoPQ6uRKCgo+NDv0QLIzMykoKDgk32B8j3w494AbN7VlllLBwFQ2aw96/qdl7q2KgqsjqFnpzY8\nffWZxHGUvh+EJEmSVE8YWo3E+y+8OOi3Du7bCj/rD8D67e15dfkAACqad2B933NS11ZEgaIY+vVo\nzzPfHk8UGViSJElqvHwZhj7aziK47RgAirZ2ZM6KvgCUt+jEhj5TUteWR4G1MQzJ6cStXx9rYEmS\nJKlB82UY+nS2LIVfHw/Aio1deHNNNgDlLbuwIWdy6tq7UWB9DMMGdOWer56ayFRJkiSprjK0VGvt\nXLhnHABL13Xn7bW9ACjL7M7G7LNT196JAptiOH5ID+69YnQiUyVJkqS6ztBq7FY8D3+o/b1Wi4p6\nsWxDdwBKW/VkU9aZqWtvR4HNMZwyNIv7LzkhkamSJElSfWFoNVaL/wx/uQKAeSuzWb2lCwAlrbPY\n3Ht86tqiOLA1gvHH9eGqC0YlMlWSJEmqbwytxmbub+GJqwB4bVk/1u3oAMC+Nn3Y0mtc6trCOLA9\ngsknDuBrU0ckMlWSJEmqrwytxuKdh+HBLxECvLRkEFv3tAVgb9t+bO35+dS1BXFgZwTnnjKY/IlD\nk1orSZIk1WuGVkM3/z54/JuEAIvW92P52tpPsPa0G8i2HmM+uBYHdkUw7bQjueyMo5NaK0mSJDUI\nhlZDNft2eO5aQoD5649k9dpMAFb0OpK4zQcvs5gbB/ZEcPmEo7lo3JFJrZUkSZIaFEOrIQkBZt4A\nL/+MmgBz1g1n7boMABZn59Aq8zTiA1fnxIG9EfzzpGGcM2ZQcpslSZKkBsjQaghqauDJf4d591BT\nEzF73Sg2ra8BYEHffrRv/nlaAdUEXouhIoKvTx3BpBMHJLtbkiRJaqAMrfqsej88nA9v/4Xq6ohZ\na09k28ZKoIY5AwbQqckptAf2E3j9QGBdf8VoRg3pkfRySZIkqUEztOqjqnL448Wwcgb7q2Nmrj6J\n3VsrgEpeHTSYbvHJdAIqCbwRQ2UEP//aWI7q2znp5ZIkSVKjYGjVJxV74f7JsH4+lfszeH7FSZTs\nrAAqeHnIkfQMJ9ANKCcwJ4aqCG775mnkZndMerkkSZLUqBha9UHpDvjtONixkoqqJjyz7EQq9lYC\nFbx41DFkVY+iZ4BSAnNj2B/Br751OgN6dUh6uSRJktQoGVp12Z6NcOcJULaDsoqmPPXO56iu2A9U\nMvOYYeRUjSSrGkoIzDsQWHdeNZ5+PY5IerkkSZLUqBladdHONXD7cAjVlJQ348mFeRAA9jPj2BH0\nqRxOThXsIbAghuoI7v7OBHK6tUt4uCRJkiQwtOqWLUvh18cDsLesBU8vHJ46em7YcfQrP5Y+lbD7\nQGDVRHDP1WfSu0vbpBZLkiRJ+giGVl2wbj789vMA7CppyXOLjgJqP8R6bvjx9C87mn7lsJPAwgOB\n9fv/OIsendokOFqSJEnS32NoJWnVS3D/JAB27G3FjLeHAO8H1on0LxtC/zLYTmBRDHGTmPu+dxZd\nO7RKcLQkSZKkj2NoJeHdJ2r/HCxg6+42vLhkMFAbWM+PGE2/0kH0L4OtBBbH0LJFE/5w9Zl0apeZ\n4GhJkiRJn5ShdTi99Ud4+J8B2LSnMy+/kwPUBtaM4afSt6w//UphC4G3Y2jTqhn/850JdGzbMrnN\nkiRJkg6aoXU4vHEXPPVdANaV9OG1RZ0ACETMHD6OPmU59C2DjVFgSQQd27Xkf68aT/vWLZJcLUmS\nJOlTMrQOpZduhhduAKCo/GjmvFkbTmVNIl47Zjw5Zb3pUwYbosDSCLp1bMWf/+102rZqnuRqSZIk\nSZ+RoZVuIcAz0+H1XwGwsnIUC+bXALCrRcxbuWfSu7w7OWWwLgosi6B317Y89M1xtG7ZLMnlkiRJ\nktLE0EqXmmp49Ovw1v8AsKxqDIvmlQI1bG6dwfL+Z9Ozogu9y6E4CrwXQd+e7Xn4a2Np1aJpstsl\nSZIkpZWh9Vntr4QHvwTLniAEWLJ/PEvm7QBKKW7flHXZk+hW2YGeFbAmCqyMYHB2Rx796im0bG5g\nSZIkSQ2RofVpVZZC4blQNJsQ4K3Ks3hvwRZgBys6NWNbz8l0qWxPt0pYFQVWR3BUv8489pUxtGjm\nf+ySJElSQ+bP+A9W+W743QTY8g4hwPzyyaxeuAHYwjtdm1PadQodq9rQpRJWRoE1MQwf2JU7vjya\nZk0zkl4vSZIk6TAwtD6pfVvhrtGwdwM1AeaUTGHt4nXABhb0akk4YipH7M+kZRW8FwWKYxiV24Nf\nfelEmjYxsCRJkqTGxND6OLvWwq+Og6pSamoiZu+Zyqala4F1vJ7Tihatp9K+ugXsh2VRYF0MJx3T\ni//64gk0yYiTXi9JkiQpAYbW37PtPfjlSACqqyNe2j6Z7Ss3AGuZ1b81RzQ/hy41zaAalkaBDTGc\nOiyL31x0PBkGliRJktSoGVr/18a3an+JIFBVHfPCprPYXbwJ2MDzg9vSPZ5Kr9AUamBJFNgYw+l5\nfbjn/DwyYgNLkiRJkqH1gaLX4N4zAKjcn8Hza0+nZNM2YBNPHtmOvuEc+oYMCPB2FNgcw1mf68c3\npo4kjqNkt0uSJEmqUwyt956rfU07UF7VhGdXjKZi115gG48ecwS5VVMZXFP7SdXiOLAlgiknD+Sr\nk4cRRQaWJEmSpL/VeEPr7Yfgz5cDUFbRjKeWjqK6rBzYy1+GdWRo+RSOrqoNqbfiwLYIzj91MFec\ndayBJUmSJOkfanyhNe9e+Ou/AVBCF558PQdCAMp5YGRnRpV8gWHltVcXxoHtEUw77UguHX+UgSVJ\nkiTpE2k8ofXKL+D56wDY07Q/z8w64sBB4A/HdeWEvZMYVVL75M04sCOCS884ii+edlQyeyVJkiTV\nWw07tEKAGT+CV34OwK7MYTw344Mf8n3Hd+Pk3RM5YW/tX8+PA7si+Kezj+X8U3OTWCxJkiSpAWiY\noVVTA098G+bfC8D2tqOZ+UxZ6vh3J/TglJ1ncfLu2r+eFwd2R3Dl5GFMGT0oicWSJEmSGpCGFVrV\nVfDQV+CdhwHYcsSZvPTkVqA2sn5zUi/Gbp/AKTtrr8+NA3si+OY5Izn7hP4JjZYkSZLU0DSM0Koq\nhwcuhFUvALCx07m88ngRsBWAu0f3ZtzWMxi7vfb6G3FgXwRXXXAc44/rm9BoSZIkSQ1V/Q+tVS/B\n/ZMAWNf5Ul57bClQBMBdo3M4betpjKvtLV6PAyURfPfi4xk3IieZvZIkSZIavPofWh37UdTrW8x5\n8BVgKeVN4N4T+zJ+y1hO+z+BNf2SExgzNCvRuZIkSZIavnofWq+/OJu1D77CrhYRfxw1gNO3jGH8\nltqzV+NAWQTXfekkTjy6V7JDJUmSJDUa9Tq0yiv38503ttBs9FBO35rH6QcCa3YcKI/g+itGM2pI\nj2RHSpIkSWp06nVozXprLTllnTmqtAvVBF6LoSKCG/PHMHJQ96TnSZIkSWqk6nVoDc7qQEWbZry4\nr5LqCH565akM7d816VmSJEmSGrl6HVpZXdvx39dOIiOOaZIRJz1HkiRJkoB6HloAzZvW+x+CJEmS\npAbGj4EkSZIkKc0MLUmSJElKM0NLkiRJktLM0JIkSZKkNDO0JEmSJCnNDC1JkiRJSjNDS5IkSZLS\nzNCSlBaFhYXk5OQQxzE5OTkUFhYmPUmSJCkx/mm/kj6zwsJC8vPzKS0tBaCoqIj8/HwApk2bluQ0\nSZKkRPiJlqTPbPr06anIel9paSnTp09PaJEkSVKyDC1Jn1lxcfFBPZckSWroDC1Jn1lWVtZBPZck\nSWroDC1Jn1lBQQGZmZkfepaZmUlBQUFCiyRJkpJlaEn6zKZNm8bdd99NdnY2URSRnZ3N3Xff7Ysw\nJElSoxWFED7x5ZEjR4Z58+YdwjmSJEmSVHdFUTQ/hDDy4+75iZYkSZIkpZmhJUmSJElpZmhJkiRJ\nUpoZWpIkSZKUZoaWJEmSJKWZoSVJkiRJaWZoSZIkSVKaGVqSJEmSlGaGliRJkiSlmaElSZIkSWlm\naEmSJElSmhlakiRJkpRmhpYkSZIkpZmhJUmSJElpZmhJkiRJUpoZWpIkSZKUZoaWJEmSJKWZoSVJ\nkiRJaWZoSZIkSVKaGVqSJEmSlGaGliRJkiSlmaElSZIkSWlmaEmSJElSmhlakiRJkpRmhpYkSZIk\npZmhJUmSJElpZmhJkiRJUpoZWpIkSZKUZoaWJEmSJKWZoSVJkiRJaWZoSZIkSVKaGVqSJEmSlGaG\nliRJkiSlmaElSZIkSWlWr0OrsLCQnJwc4jgmJyeHwsLCpCdJkiRJEk2SHvBpFRYWkp+fT2lpZ5Lu\ntgAAAbJJREFUKQBFRUXk5+cDMG3atCSnSZIkSWrk6u0nWtOnT09F1vtKS0uZPn16QoskSZIkqVa9\nDa3i4uKDei5JkiRJh0u9Da2srKyDei5JkiRJh0u9Da2CggIyMzM/9CwzM5OCgoKEFkmSJElSrXob\nWtOmTePuu+8mOzubKIrIzs7m7rvv9kUYkiRJkhIXhRA+8eWRI0eGefPmHcI5kiRJklR3RVE0P4Qw\n8uPu1dtPtCRJkiSprjK0JEmSJCnNDC1JkiRJSjNDS5IkSZLSzNCSJEmSpDQztCRJkiQpzQwtSZIk\nSUozQ0uSJEmS0szQkiRJkqQ0M7QkSZIkKc0MLUmSJElKM0NLkiRJktLM0JIkSZKkNDO0JEmSJCnN\nDC1JkiRJSjNDS5IkSZLSzNCSJEmSpDQztCRJkiQpzQwtSZIkSUozQ0uSJEmS0iwKIXzyy1G0FSg6\ndHMkSZIkqU7LDiF0/rhLBxVakiRJkqSP5y8dlCRJkqQ0M7QkSZIkKc0MLUmSJElKM0NLkiRJktLM\n0JIkSZKkNDO0JEmSJCnNDC1JkiRJSjNDS5IkSZLSzNCSJEmSpDT7f+yylaOLRy0UAAAAAElFTkSu\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6d5cda1e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficients: \n",
      " [ 0.5] [ 0.49594229] [ 0.49] [ 0.49454906] [ 0.5]\n",
      "Super parameters: \n",
      " () (0.90000000000000002,) (0.10000000000000001,) (0.10000000000000001, 0.10000000000000001) (0.0,)\n",
      "Mean squared error: \n",
      " 1.25 1.25 1.25 1.25 1.25\n",
      "Variance score: \n",
      " 0.67 0.67 0.67 0.67 0.67\n"
     ]
    },
    {
     "data": {
      "image/png": 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1q3JyclSmzLUfgc1mU+/evfXYY48pNjZWkrRp0yYdPnxYAwcOVGxsrF577TVJ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\nA8nBhLPQmmPJAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6d5cd3ce80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficients: \n",
      " [ 0.49972727] [ 0.49567178] [ 0.40972727] [ 0.45395677] [ 0.49972727]\n",
      "Super parameters: \n",
      " () (0.90000000000000002,) (0.90000000000000002,) (0.90000000000000002, 0.10000000000000001) (0.0,)\n",
      "Mean squared error: \n",
      " 1.25 1.25 1.33 1.27 1.25\n",
      "Variance score: \n",
      " 0.67 0.67 0.64 0.66 0.67\n"
     ]
    },
    {
     "data": {
      "image/png": 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SpFyxcOWP3PyfD4kIwQn7HQ+ccPxHfB+zEYD/nH4LXeudnF/LzBUGLUmSJEmB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\niIj4mIKWiIiIiIiIj/0/aYcKGmpj73oAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6d5cf50ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficients: \n",
      " [ 0.49990909] [ 0.49585212] [ 0.46990909] [ 0.46369623] [ 0.49990909]\n",
      "Super parameters: \n",
      " () (0.90000000000000002,) (0.29999999999999999,) (0.69999999999999996, 0.10000000000000001) (0.0,)\n",
      "Mean squared error: \n",
      " 1.25 1.25 1.26 1.26 1.25\n",
      "Variance score: \n",
      " 0.67 0.67 0.66 0.66 0.67\n"
     ]
    },
    {
     "data": {
      "image/png": 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L1+k9eRVOBtSxgmdGwTpmMjjllEbc41uI4zIAA2q8wKBanbK/YFmtsHo4bJ9u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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6d5cde8d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# The data is from https://gist.github.com/endolith/3299951 , might change to use\n",
    "# the dataset in sns instead: https://seaborn.pydata.org/examples/anscombes_quartet.html\n",
    "x1 = np.array([10.0, 8.0,  13.0,  9.0,  11.0, 14.0, 6.0,  4.0,  12.0,  7.0,  5.0]).reshape(-1, 1)\n",
    "y1 = np.array([8.04, 6.95, 7.58,  8.81, 8.33, 9.96, 7.24, 4.26, 10.84, 4.82, 5.68])\n",
    "\n",
    "x2 = np.array([10.0, 8.0,  13.0,  9.0,  11.0, 14.0, 6.0,  4.0,  12.0,  7.0,  5.0]).reshape(-1, 1)\n",
    "y2 = np.array([9.14, 8.14, 8.74,  8.77, 9.26, 8.10, 6.13, 3.10, 9.13,  7.26, 4.74])\n",
    "\n",
    "x3 = np.array([10.0, 8.0,  13.0,  9.0,  11.0, 14.0, 6.0,  4.0,  12.0,  7.0,  5.0]).reshape(-1, 1)\n",
    "y3 = np.array([7.46, 6.77, 12.74, 7.11, 7.81, 8.84, 6.08, 5.39, 8.15,  6.42, 5.73])\n",
    "\n",
    "x4 = np.array([8.0,  8.0,  8.0,   8.0,  8.0,  8.0,  8.0,  19.0,  8.0,  8.0,  8.0]).reshape(-1, 1)\n",
    "y4 = np.array([6.58, 5.76, 7.71,  8.84, 8.47, 7.04, 5.25, 12.50, 5.56, 7.91, 6.89])\n",
    "\n",
    "fit_and_plot(x1, y1)\n",
    "fit_and_plot(x2, y2)\n",
    "fit_and_plot(x3, y3)\n",
    "fit_and_plot(x4, y4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "L1-norm:\n",
    "\n",
    "$$ \\left\\| \\boldsymbol{x} \\right\\| _1 := \\sum_{i=1}^{n} \\left| x_i \\right| $$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>bmi</th>\n",
       "      <th>bp</th>\n",
       "      <th>s1</th>\n",
       "      <th>s2</th>\n",
       "      <th>s3</th>\n",
       "      <th>s4</th>\n",
       "      <th>s5</th>\n",
       "      <th>s6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>151.0</th>\n",
       "      <td>0.038076</td>\n",
       "      <td>0.050680</td>\n",
       "      <td>0.061696</td>\n",
       "      <td>0.021872</td>\n",
       "      <td>-0.044223</td>\n",
       "      <td>-0.034821</td>\n",
       "      <td>-0.043401</td>\n",
       "      <td>-0.002592</td>\n",
       "      <td>0.019908</td>\n",
       "      <td>-0.017646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75.0</th>\n",
       "      <td>-0.001882</td>\n",
       "      <td>-0.044642</td>\n",
       "      <td>-0.051474</td>\n",
       "      <td>-0.026328</td>\n",
       "      <td>-0.008449</td>\n",
       "      <td>-0.019163</td>\n",
       "      <td>0.074412</td>\n",
       "      <td>-0.039493</td>\n",
       "      <td>-0.068330</td>\n",
       "      <td>-0.092204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141.0</th>\n",
       "      <td>0.085299</td>\n",
       "      <td>0.050680</td>\n",
       "      <td>0.044451</td>\n",
       "      <td>-0.005671</td>\n",
       "      <td>-0.045599</td>\n",
       "      <td>-0.034194</td>\n",
       "      <td>-0.032356</td>\n",
       "      <td>-0.002592</td>\n",
       "      <td>0.002864</td>\n",
       "      <td>-0.025930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206.0</th>\n",
       "      <td>-0.089063</td>\n",
       "      <td>-0.044642</td>\n",
       "      <td>-0.011595</td>\n",
       "      <td>-0.036656</td>\n",
       "      <td>0.012191</td>\n",
       "      <td>0.024991</td>\n",
       "      <td>-0.036038</td>\n",
       "      <td>0.034309</td>\n",
       "      <td>0.022692</td>\n",
       "      <td>-0.009362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135.0</th>\n",
       "      <td>0.005383</td>\n",
       "      <td>-0.044642</td>\n",
       "      <td>-0.036385</td>\n",
       "      <td>0.021872</td>\n",
       "      <td>0.003935</td>\n",
       "      <td>0.015596</td>\n",
       "      <td>0.008142</td>\n",
       "      <td>-0.002592</td>\n",
       "      <td>-0.031991</td>\n",
       "      <td>-0.046641</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            age       sex       bmi        bp        s1        s2        s3  \\\n",
       "151.0  0.038076  0.050680  0.061696  0.021872 -0.044223 -0.034821 -0.043401   \n",
       "75.0  -0.001882 -0.044642 -0.051474 -0.026328 -0.008449 -0.019163  0.074412   \n",
       "141.0  0.085299  0.050680  0.044451 -0.005671 -0.045599 -0.034194 -0.032356   \n",
       "206.0 -0.089063 -0.044642 -0.011595 -0.036656  0.012191  0.024991 -0.036038   \n",
       "135.0  0.005383 -0.044642 -0.036385  0.021872  0.003935  0.015596  0.008142   \n",
       "\n",
       "             s4        s5        s6  \n",
       "151.0 -0.002592  0.019908 -0.017646  \n",
       "75.0  -0.039493 -0.068330 -0.092204  \n",
       "141.0 -0.002592  0.002864 -0.025930  \n",
       "206.0  0.034309  0.022692 -0.009362  \n",
       "135.0 -0.002592 -0.031991 -0.046641  "
      ]
     },
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>bmi</th>\n",
       "      <th>bp</th>\n",
       "      <th>s1</th>\n",
       "      <th>s2</th>\n",
       "      <th>s3</th>\n",
       "      <th>s4</th>\n",
       "      <th>s5</th>\n",
       "      <th>s6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4.420000e+02</td>\n",
       "      <td>4.420000e+02</td>\n",
       "      <td>4.420000e+02</td>\n",
       "      <td>4.420000e+02</td>\n",
       "      <td>4.420000e+02</td>\n",
       "      <td>4.420000e+02</td>\n",
       "      <td>4.420000e+02</td>\n",
       "      <td>4.420000e+02</td>\n",
       "      <td>4.420000e+02</td>\n",
       "      <td>4.420000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-3.639623e-16</td>\n",
       "      <td>1.309912e-16</td>\n",
       "      <td>-8.013951e-16</td>\n",
       "      <td>1.289818e-16</td>\n",
       "      <td>-9.042540e-17</td>\n",
       "      <td>1.301121e-16</td>\n",
       "      <td>-4.563971e-16</td>\n",
       "      <td>3.863174e-16</td>\n",
       "      <td>-3.848103e-16</td>\n",
       "      <td>-3.398488e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.761905e-02</td>\n",
       "      <td>4.761905e-02</td>\n",
       "      <td>4.761905e-02</td>\n",
       "      <td>4.761905e-02</td>\n",
       "      <td>4.761905e-02</td>\n",
       "      <td>4.761905e-02</td>\n",
       "      <td>4.761905e-02</td>\n",
       "      <td>4.761905e-02</td>\n",
       "      <td>4.761905e-02</td>\n",
       "      <td>4.761905e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-1.072256e-01</td>\n",
       "      <td>-4.464164e-02</td>\n",
       "      <td>-9.027530e-02</td>\n",
       "      <td>-1.123996e-01</td>\n",
       "      <td>-1.267807e-01</td>\n",
       "      <td>-1.156131e-01</td>\n",
       "      <td>-1.023071e-01</td>\n",
       "      <td>-7.639450e-02</td>\n",
       "      <td>-1.260974e-01</td>\n",
       "      <td>-1.377672e-01</td>\n",
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       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-3.729927e-02</td>\n",
       "      <td>-4.464164e-02</td>\n",
       "      <td>-3.422907e-02</td>\n",
       "      <td>-3.665645e-02</td>\n",
       "      <td>-3.424784e-02</td>\n",
       "      <td>-3.035840e-02</td>\n",
       "      <td>-3.511716e-02</td>\n",
       "      <td>-3.949338e-02</td>\n",
       "      <td>-3.324879e-02</td>\n",
       "      <td>-3.317903e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.383060e-03</td>\n",
       "      <td>-4.464164e-02</td>\n",
       "      <td>-7.283766e-03</td>\n",
       "      <td>-5.670611e-03</td>\n",
       "      <td>-4.320866e-03</td>\n",
       "      <td>-3.819065e-03</td>\n",
       "      <td>-6.584468e-03</td>\n",
       "      <td>-2.592262e-03</td>\n",
       "      <td>-1.947634e-03</td>\n",
       "      <td>-1.077698e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.807591e-02</td>\n",
       "      <td>5.068012e-02</td>\n",
       "      <td>3.124802e-02</td>\n",
       "      <td>3.564384e-02</td>\n",
       "      <td>2.835801e-02</td>\n",
       "      <td>2.984439e-02</td>\n",
       "      <td>2.931150e-02</td>\n",
       "      <td>3.430886e-02</td>\n",
       "      <td>3.243323e-02</td>\n",
       "      <td>2.791705e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.107267e-01</td>\n",
       "      <td>5.068012e-02</td>\n",
       "      <td>1.705552e-01</td>\n",
       "      <td>1.320442e-01</td>\n",
       "      <td>1.539137e-01</td>\n",
       "      <td>1.987880e-01</td>\n",
       "      <td>1.811791e-01</td>\n",
       "      <td>1.852344e-01</td>\n",
       "      <td>1.335990e-01</td>\n",
       "      <td>1.356118e-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "                age           sex           bmi            bp            s1  \\\n",
       "count  4.420000e+02  4.420000e+02  4.420000e+02  4.420000e+02  4.420000e+02   \n",
       "mean  -3.639623e-16  1.309912e-16 -8.013951e-16  1.289818e-16 -9.042540e-17   \n",
       "std    4.761905e-02  4.761905e-02  4.761905e-02  4.761905e-02  4.761905e-02   \n",
       "min   -1.072256e-01 -4.464164e-02 -9.027530e-02 -1.123996e-01 -1.267807e-01   \n",
       "25%   -3.729927e-02 -4.464164e-02 -3.422907e-02 -3.665645e-02 -3.424784e-02   \n",
       "50%    5.383060e-03 -4.464164e-02 -7.283766e-03 -5.670611e-03 -4.320866e-03   \n",
       "75%    3.807591e-02  5.068012e-02  3.124802e-02  3.564384e-02  2.835801e-02   \n",
       "max    1.107267e-01  5.068012e-02  1.705552e-01  1.320442e-01  1.539137e-01   \n",
       "\n",
       "                 s2            s3            s4            s5            s6  \n",
       "count  4.420000e+02  4.420000e+02  4.420000e+02  4.420000e+02  4.420000e+02  \n",
       "mean   1.301121e-16 -4.563971e-16  3.863174e-16 -3.848103e-16 -3.398488e-16  \n",
       "std    4.761905e-02  4.761905e-02  4.761905e-02  4.761905e-02  4.761905e-02  \n",
       "min   -1.156131e-01 -1.023071e-01 -7.639450e-02 -1.260974e-01 -1.377672e-01  \n",
       "25%   -3.035840e-02 -3.511716e-02 -3.949338e-02 -3.324879e-02 -3.317903e-02  \n",
       "50%   -3.819065e-03 -6.584468e-03 -2.592262e-03 -1.947634e-03 -1.077698e-03  \n",
       "75%    2.984439e-02  2.931150e-02  3.430886e-02  3.243323e-02  2.791705e-02  \n",
       "max    1.987880e-01  1.811791e-01  1.852344e-01  1.335990e-01  1.356118e-01  "
      ]
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     "metadata": {},
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    }
   ],
   "source": [
    "# Adapted from http://scikit-learn.org/stable/auto_examples/linear_model/plot_lasso_lars.html#sphx-glr-auto-examples-linear-model-plot-lasso-lars-py\n",
    "# Author: Fabian Pedregosa <fabian.pedregosa@inria.fr>\n",
    "#         Alexandre Gramfort <alexandre.gramfort@inria.fr>\n",
    "# License: BSD 3 clause\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "from sklearn import linear_model\n",
    "from sklearn import datasets\n",
    "\n",
    "diabetes = datasets.load_diabetes()\n",
    "X = diabetes.data\n",
    "y = diabetes.target\n",
    "\n",
    "df = pd.DataFrame(X, index=y, columns=diabetes.feature_names)\n",
    "display(df.head())\n",
    "display(df.describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computing regularization path using the LARS ...\n",
      "."
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    },
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       "      <td>434.757960</td>\n",
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       "      <td>526.390594</td>\n",
       "      <td>522.648786</td>\n",
       "      <td>521.075130</td>\n",
       "      <td>519.839787</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>79.236447</td>\n",
       "      <td>191.269884</td>\n",
       "      <td>234.154616</td>\n",
       "      <td>252.527017</td>\n",
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       "      <td>321.549027</td>\n",
       "      <td>324.390428</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-103.946249</td>\n",
       "      <td>-195.105830</td>\n",
       "      <td>-237.340973</td>\n",
       "      <td>-554.266328</td>\n",
       "      <td>-580.438600</td>\n",
       "      <td>-792.184162</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>286.736168</td>\n",
       "      <td>313.862132</td>\n",
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       "      <td>-169.711394</td>\n",
       "      <td>-196.045443</td>\n",
       "      <td>-223.926033</td>\n",
       "      <td>-152.477259</td>\n",
       "      <td>-134.599352</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>106.342806</td>\n",
       "      <td>111.384129</td>\n",
       "      <td>148.900445</td>\n",
       "      <td>139.857868</td>\n",
       "      <td>177.064176</td>\n",
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       "      <td>663.033287</td>\n",
       "      <td>674.936617</td>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>54.767681</td>\n",
       "      <td>64.487418</td>\n",
       "      <td>64.606670</td>\n",
       "      <td>66.330955</td>\n",
       "      <td>67.179400</td>\n",
       "      <td>67.625386</td>\n",
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       "3  0.0   0.00000    0.000000   79.236447  191.269884  234.154616  252.527017   \n",
       "4  0.0   0.00000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "5  0.0   0.00000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
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       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10.0</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.0</td>\n",
       "      <td>6.011927</td>\n",
       "      <td>66.366996</td>\n",
       "      <td>88.891024</td>\n",
       "      <td>102.249340</td>\n",
       "      <td>95.154223</td>\n",
       "      <td>92.101799</td>\n",
       "      <td>86.331296</td>\n",
       "      <td>96.830424</td>\n",
       "      <td>99.732661</td>\n",
       "      <td>121.360930</td>\n",
       "      <td>121.390954</td>\n",
       "      <td>137.597406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.0</td>\n",
       "      <td>19.011382</td>\n",
       "      <td>140.629650</td>\n",
       "      <td>168.930834</td>\n",
       "      <td>209.244879</td>\n",
       "      <td>226.881654</td>\n",
       "      <td>234.972742</td>\n",
       "      <td>269.681995</td>\n",
       "      <td>277.171838</td>\n",
       "      <td>282.475949</td>\n",
       "      <td>355.759653</td>\n",
       "      <td>364.973745</td>\n",
       "      <td>435.780706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-114.100980</td>\n",
       "      <td>-169.711394</td>\n",
       "      <td>-196.045443</td>\n",
       "      <td>-223.926033</td>\n",
       "      <td>-226.133662</td>\n",
       "      <td>-237.340973</td>\n",
       "      <td>-554.266328</td>\n",
       "      <td>-580.438600</td>\n",
       "      <td>-792.184162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-77.959687</td>\n",
       "      <td>-114.357945</td>\n",
       "      <td>-100.949514</td>\n",
       "      <td>-4.289211</td>\n",
       "      <td>-5.258434</td>\n",
       "      <td>9.397198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>32.243709</td>\n",
       "      <td>49.117472</td>\n",
       "      <td>107.615700</td>\n",
       "      <td>103.518634</td>\n",
       "      <td>139.054373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>59.427335</td>\n",
       "      <td>143.452413</td>\n",
       "      <td>175.615962</td>\n",
       "      <td>192.414800</td>\n",
       "      <td>236.561723</td>\n",
       "      <td>262.377655</td>\n",
       "      <td>264.058883</td>\n",
       "      <td>311.940958</td>\n",
       "      <td>319.627303</td>\n",
       "      <td>438.656985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.0</td>\n",
       "      <td>60.119270</td>\n",
       "      <td>361.894612</td>\n",
       "      <td>434.757960</td>\n",
       "      <td>505.659558</td>\n",
       "      <td>511.348071</td>\n",
       "      <td>512.044089</td>\n",
       "      <td>522.264847</td>\n",
       "      <td>529.916031</td>\n",
       "      <td>545.482597</td>\n",
       "      <td>663.033287</td>\n",
       "      <td>674.936617</td>\n",
       "      <td>751.279321</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         0          1           2           3           4           5   \\\n",
       "count  10.0  10.000000   10.000000   10.000000   10.000000   10.000000   \n",
       "mean    0.0   6.011927   66.366996   88.891024  102.249340   95.154223   \n",
       "std     0.0  19.011382  140.629650  168.930834  209.244879  226.881654   \n",
       "min     0.0   0.000000    0.000000    0.000000 -114.100980 -169.711394   \n",
       "25%     0.0   0.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "50%     0.0   0.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "75%     0.0   0.000000    0.000000   59.427335  143.452413  175.615962   \n",
       "max     0.0  60.119270  361.894612  434.757960  505.659558  511.348071   \n",
       "\n",
       "               6           7           8           9           10          11  \\\n",
       "count   10.000000   10.000000   10.000000   10.000000   10.000000   10.000000   \n",
       "mean    92.101799   86.331296   96.830424   99.732661  121.360930  121.390954   \n",
       "std    234.972742  269.681995  277.171838  282.475949  355.759653  364.973745   \n",
       "min   -196.045443 -223.926033 -226.133662 -237.340973 -554.266328 -580.438600   \n",
       "25%      0.000000  -77.959687 -114.357945 -100.949514   -4.289211   -5.258434   \n",
       "50%      0.000000    0.000000   32.243709   49.117472  107.615700  103.518634   \n",
       "75%    192.414800  236.561723  262.377655  264.058883  311.940958  319.627303   \n",
       "max    512.044089  522.264847  529.916031  545.482597  663.033287  674.936617   \n",
       "\n",
       "               12  \n",
       "count   10.000000  \n",
       "mean   137.597406  \n",
       "std    435.780706  \n",
       "min   -792.184162  \n",
       "25%      9.397198  \n",
       "50%    139.054373  \n",
       "75%    438.656985  \n",
       "max    751.279321  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "      <th>2</th>\n",
       "      <th>3</th>\n",
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       "      <th>9</th>\n",
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       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>|coef|</th>\n",
       "      <td>0.0</td>\n",
       "      <td>60.119270</td>\n",
       "      <td>663.669955</td>\n",
       "      <td>888.910243</td>\n",
       "      <td>1250.695364</td>\n",
       "      <td>1440.798043</td>\n",
       "      <td>1537.065983</td>\n",
       "      <td>1914.570529</td>\n",
       "      <td>2115.737744</td>\n",
       "      <td>2195.558855</td>\n",
       "      <td>2802.375093</td>\n",
       "      <td>2863.010804</td>\n",
       "      <td>3460.004955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>|coef|/max(|coef|)</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.017375</td>\n",
       "      <td>0.191812</td>\n",
       "      <td>0.256910</td>\n",
       "      <td>0.361472</td>\n",
       "      <td>0.416415</td>\n",
       "      <td>0.444238</td>\n",
       "      <td>0.553343</td>\n",
       "      <td>0.611484</td>\n",
       "      <td>0.634554</td>\n",
       "      <td>0.809934</td>\n",
       "      <td>0.827459</td>\n",
       "      <td>1.000000</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     0          1           2           3            4   \\\n",
       "|coef|              0.0  60.119270  663.669955  888.910243  1250.695364   \n",
       "|coef|/max(|coef|)  0.0   0.017375    0.191812    0.256910     0.361472   \n",
       "\n",
       "                             5            6            7            8   \\\n",
       "|coef|              1440.798043  1537.065983  1914.570529  2115.737744   \n",
       "|coef|/max(|coef|)     0.416415     0.444238     0.553343     0.611484   \n",
       "\n",
       "                             9            10           11           12  \n",
       "|coef|              2195.558855  2802.375093  2863.010804  3460.004955  \n",
       "|coef|/max(|coef|)     0.634554     0.809934     0.827459     1.000000  "
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    },
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Alphas</th>\n",
       "      <td>2.148044</td>\n",
       "      <td>2.012027</td>\n",
       "      <td>1.024663</td>\n",
       "      <td>0.7151</td>\n",
       "      <td>0.294414</td>\n",
       "      <td>0.200865</td>\n",
       "      <td>0.15603</td>\n",
       "      <td>0.045206</td>\n",
       "      <td>0.012392</td>\n",
       "      <td>0.011514</td>\n",
       "      <td>0.004937</td>\n",
       "      <td>0.002965</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              0         1         2       3         4         5        6   \\\n",
       "Alphas  2.148044  2.012027  1.024663  0.7151  0.294414  0.200865  0.15603   \n",
       "\n",
       "              7         8         9         10        11   12  \n",
       "Alphas  0.045206  0.012392  0.011514  0.004937  0.002965  0.0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Computing regularization path using the LARS ...\")\n",
    "alphas, _, coefs = linear_model.lars_path(X, y, method='lasso', verbose=True)\n",
    "\n",
    "df = pd.DataFrame(coefs)\n",
    "display(df)\n",
    "display(df.describe())\n",
    "\n",
    "# xx are the L1-norms\n",
    "xx = np.sum(np.abs(coefs.T), axis=1)\n",
    "\n",
    "# the last of xx is the maximum of L1-norms\n",
    "normalized_xx = xx / xx[-1]\n",
    "\n",
    "df = pd.DataFrame(np.array([xx, normalized_xx]), index=[\"|coef|\", \"|coef|/max(|coef|)\"])\n",
    "display(df)\n",
    "\n",
    "df = pd.DataFrame(alphas.reshape(1, -1), index=[\"Alphas\"])\n",
    "display(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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kSZLJJHEbP/M7IOsPMkP9IWfgjzGxZIyB+2k78H9Zrwdy2IFj2bGfXZdzng9R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\nRlNDO1uXH2PvmjqCgSDDJ4d6JWYXJ1sdW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92fYDDmYJC3q+uV+M4AzgY24XZ07Orvl/4B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\nkiRJWBxKkiRJkrA4lCRJkiQB/w+/CbtzbCRCFAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6d58aa7208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(xx, coefs.T)\n",
    "ymin, ymax = plt.ylim()\n",
    "plt.vlines(xx, ymin, ymax, linestyle='dashed')\n",
    "plt.xlabel('|coef|')\n",
    "plt.ylabel('coefs')\n",
    "plt.title('LASSO Path')\n",
    "plt.axis('tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From https://stats.stackexchange.com/a/4938 :\n",
    "\n",
    "> In scikit-learn the implementation of Lasso with coordinate descent tends to be faster than our implementation of LARS although for small p (such as in your case) they are roughly equivalent (LARS might even be a bit faster with the latest optimizations available in the master repo). Furthermore coordinate descent allows for efficient implementation of elastic net regularized problems. This is not the case for LARS (that solves only Lasso, aka L1 penalized problems).\n",
    "\n",
    "> Elastic Net penalization tends to yield a better generalization than Lasso (closer to the solution of ridge regression) while keeping the nice sparsity inducing features of Lasso (supervised feature selection).\n",
    "\n",
    "> For large N (and large p, sparse or not) you might also give a stochastic gradient descent (with L1 or elastic net penalty) a try (also implemented in scikit-learn)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
